Flowmeter array processing algorithm with wide dynamic range

ABSTRACT

Methods and apparatus enable sensing flow of a fluid inside a conduit with an array of pressure or strain sensors. Inputs for a curve fit routine include power correlation values at one of multiple trial velocities or speeds of sound and several steps on either side utilizing data obtained from the sensors. The velocity at which a curve fit routine returns a max curvature result corresponds to an estimate value that facilitates identification of a speed of sound in the fluid and/or a velocity of the flow. Furthermore, a directional quality compensation factor may apply to outputs from the curve fit routine to additionally aid in determining the velocity of the flow.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/025,294, filed Feb. 4, 2008, now U.S. Pat. No. 7,881,884, whichclaims benefit of U.S. provisional patent application Ser. No.60/888,426, filed Feb. 6, 2007, which are both herein incorporated byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Embodiments of the invention generally relate to flow sensing with anarray of pressure or strain sensors.

2. Description of the Related Art

A flowmeter consisting of an array of dynamic strain sensors mounted onthe exterior of a pipe employs an array processing algorithm applied tosignals from the sensors in order to estimate the velocity of pressurewaves caused by acoustics in a fluid or turbulent eddies traveling withthe fluid passing through the interior of the pipe. In application,time-series sensor signals are transformed to the frequency domain and avelocity reading is calculated by determining the time delay at whichthe coherence correlation of the sensors is maximized. Selecting afrequency range that includes the majority of the energy created by thepressure waves of interest but avoids spatial aliasing and rejectsout-of-band noise can improve performance of the flowmeter.

These frequency limits may correspond to a reduced range of flow ratesbased on fluid density, such as 0.7 to 10.0 meters per second (m/s) ifthe expected fluids are liquids (water/oil) or 3.0 to 50.0 m/s if thefluid is mostly gas. However, this approach limits ability to achieveaccurate performance over a wide dynamic range of flow velocities usinga fixed-length sensor array, and requiring no manual adjustments as isdesired. Further, a fixed frequency configuration may yield correctreadings for only a very narrow range of flow rates or fail altogetherin challenging conditions, such as gas at low flow rates combined withhigh acoustic noise levels caused by pumps or control valves, forexample.

Therefore, there exists a need for an improved flow meter and methods ofprocessing signals from sensors of the meter to determine output values.

SUMMARY OF THE INVENTION

Embodiments of the invention generally relate to flow sensing with anarray of pressure or strain sensors coupled to a conduit in which afluid is flowing. Finding an approximate flow velocity of the fluidbegins by dividing a range of possible flow rates into coarse stepswith, for example, each approximately 5% higher than the previous one.For each step, a range of frequencies selected for analysis avoidsspatial aliasing and common-mode noise. An inverse cross spectraldensity (CSD) matrix is probed at velocity intervals above and below thecoarse step value. In some embodiments, a second-order least-squarescurve fit algorithm applied to these points enables determination of the“curvature” of a power correlation around each velocity step. Thenegative of a second-order coefficient of the curve fit equation mayrepresent the “curvature” value.

A “directional quality” metric may also be calculated for each coarsevelocity step by calculating power correlations for the positive andnegative directions. The difference of these values is divided by theirsum, yielding a number between −1 and 1. Values near zero denote poorquality, where the power in both directions is nearly equal. Theabsolute value of this quality metric is multiplied by the curvaturevalue, and the velocity at which this product is highest is used as astarting guess in a progressive search routine. A similar approachwithout the “directional quality” metric facilitates determination of aspeed of sound in the fluid.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 is a flowmeter including an array of pressure sensors that arecoupled to a conduit and a processing unit that is configured to receivesignals from the pressure sensors and process the signals, according toembodiments of the invention.

FIG. 2 is a k-ω (wave number-frequency) plot graphically representingdata generated by the pressure sensors and from which a velocity of flowthrough the conduit may be derived, according to embodiments of theinvention.

FIG. 3 is another k-ω plot graphically representing data generated bythe pressure sensors and from which a speed of sound in fluid inside theconduit may be derived, according to embodiments of the invention.

FIG. 4 is a flow diagram illustrating methods of determining thevelocity and speed of sound according to embodiments of the invention.

FIG. 5 is another flow diagram illustrating a process of determining aninitial velocity (or speed of sound) estimate utilized in the methodsdepicted by FIG. 4, according to embodiments of the invention.

FIG. 6 is a plot of products obtained by multiplying a respectivedirectional quality compensation factor times a max value of powercurvature for each velocity step evaluated according to the processdepicted in FIG. 5 versus velocity.

FIG. 7 is a plot of power versus velocity, which is obtained by a totalscan of converted data from the sensors and a targeted scan of theconverted data around the velocity estimate picked by results plotted inFIG. 6 to identify a final velocity utilizing a peak of the targetedscan.

DETAILED DESCRIPTION

Embodiments of the invention relate to sensing flow of a fluid with anarray of pressure or strain sensors. For some embodiments, the sensingoccurs along a conduit carrying hydrocarbons from a producing well suchthat the sensors may be disposed in a borehole or on production pipeafter exiting the borehole. Inputs for a curve fit routine include powercorrelation values at one of multiple trial velocities or speeds ofsound and several steps on either side utilizing data obtained from thesensors. The curve fit routine with a max curvature corresponds to anestimate value that facilitates identification of a speed of sound inthe fluid and/or a velocity of the flow. Furthermore, a directionalquality compensation factor may apply to outputs from the curve fitroutine to additionally aid in determining the velocity of the flow.

FIG. 1 illustrates a flowmeter 100 that includes first, second and thirdpressure sensors 101, 102, 103 located respectively at three locationsx₁, x₂, x₃ spaced along a conduit 104 such as tubing or a pipe. Asdescribed in U.S. Pat. No. 6,354,147, which is herein incorporated byreference, the pressure sensors 101, 102, 103 may include optical fiberwrapped around an outer diameter of the conduit 104, piezoelectric(e.g., polyvinylidene fluoride), capacitive, or resistive measuringdevices or other types of optical or electrical strain gauges. Thepressure sensors 101, 102, 103 provide pressure time-varying signalsP₁(t), P₂(t), P₃(t) on lines 106 to a fluid parameter processing unit108 of the flowmeter 100 for accomplishing functions, which may beimplemented in software (using a microprocessor or computer) and/orfirmware, or may be implemented using analog and/or digital hardware,having sufficient memory, interfaces, and capacity to perform thefunctions described herein. In some embodiments, physical computerreadable storage medium of the processing unit 108 may containinstructions for such functions.

The flowmeter 100 enables measuring one or both of two fundamentalparameters that directly relate to the flow properties of a fluid 105and include (1) the speed at which pressure waves propagate through thefluid 105, the speed of sound (SoS), and (2) the convection velocity ofthe fluid 105. These values can be determined by measuring dynamicpressures in the fluid using the pressure sensors 101, 102, 103. Dynamicpressure measurements from the sensors 101, 102, 103 are then processedutilizing array processing techniques to extract at least one of thespeed of sound and the flow velocity. The flowmeter 100 may consist ofeither (1) a single array of the sensors 101, 102, 103 that are equallyspaced or (2) two arrays with different spacing (i.e., one spacing formeasuring the speed of sound and another spacing for measuring vorticalvelocity). If the speed-of-sound sensor spacings are chosen to be aninteger multiple of the vortical velocity array spacing, then the twoarrays may share sensors. In some embodiments, each array may containfewer or more than the first, second and third sensors 101, 102, 103.

While the acoustic pressure disturbances move through the fluid 105 atthe speed of sound, the vortical pressure disturbances move with thefluid 105 at the flowing velocity. In addition, the acoustic pressuredisturbances propagate through the flowmeter 100 in both directionsassuming there are acoustic sources on both sides of the flowmeter 100or acoustic reflections, while the vortical pressure disturbancespropagate through the flowmeter 100 only in one direction, which is theflowing direction. However, both the acoustic and vortical pressuredisturbances strain the wall of the conduit 104 independently andsimultaneously and so the signal measured by the sensors 101, 102, 103contains a superposition of both these signals (and possibly others suchas vibration). The amplitude of the vortical signal may be much lessthan the acoustic signal, so there may be a need to reduce the acousticpart of the overall signal such that the vortical part is exposed.Processing of vortical and acoustic pressure signals may thus requiredifferent treatment even though the same basic processing method is usedfor both.

The processing unit 108 includes Fast Fourier Transform (FFT) logic 110that initially receives the pressure time-varying signals P₁(t), P₂(t),P₃(t) from the pressure sensors 101, 102, 103. The FFT logic 108calculates the Fourier transform of blocks of data from the time-basedinput signals P₁(t), P₂(t), P₃(t) of individual ones of the sensors 101,102, 103 and provides complex frequency domain (or frequency based)signals P₁(ω), P₂(ω), P₃(ω) on lines 112 indicative of the frequencycontent of the input signals. Because the vortical flow velocity isderived from a lower frequency range than the speed-of-sound, largerblock sizes may be used for the vortical velocity, providing moreresolution in that frequency range. Instead of FFT's, any othertechnique for obtaining the frequency domain characteristics of thepressure time-varying signals P₁(t), P₂(t), P₃(t) may be used. Forexample, a cross-spectral density (CSD) and power spectral density maybe used to form a frequency domain transfer function or frequencyresponse or ratios.

For the flow velocity processing, differencing adjacent ones of thesensors 101, 102, 103 can subtract common-mode noise and reduce thenumber of signals, N, by one. Once transformed into the frequencydomain, a CSD function is applied resulting in a complex N×N matrix foreach frequency bin produced by the transform, where N is the number ofthe sensors 101, 102, 103 in the array minus one. Each N×N matrix isthen inverted. As explained further herein, probing this set of invertedmatrices occurs using the processing unit 108 to produce curvaturescorresponding to trial velocities in a first pass of the matrix withcalculation logic 114. The processing unit 108 further may fine tune aresult based on the curvatures during subsequent passes at increasinglyfiner resolution with ridge identifier logic 116. An output 118 of theprocessing unit 108 may communicate the result (e.g., the velocityand/or the speed of sound) to a user via, for example, a display orprintout. Further, the output 118 may generate a signal or control adevice based on the result.

FIGS. 2 and 3 illustrate three dimensional k-ω plots employed tovisualize the contents of the inverse CSD matrix. FIG. 2 depicts a ridgeof increased power around flow velocity line 200 corresponding to avelocity of the flow while FIG. 3 shows ridges of increased power aroundsound velocity lines 300, 302 associated with the speed of sound. TheseFIGS. 2 and 3 depict plots of the power correlation of the inverse CSDmatrix as a function of frequency ω and wave number k (phase shift) withrelative power of certain contour lines identified only in FIG. 2 forillustration purposes. Slopes of the lines 200, 300, 302 revealrespective velocities (V) according to the following equation:

$V = {\frac{\omega}{k} = {\frac{2\pi\; f}{k}.}}$

Output from a Capon algorithm scan of the inverse CSD matrix showsvelocity versus power by sampling power correlations through a range ofvelocities. Several other array processing algorithms exist (e.g. crosscorrelation Beam scan, MUSIC, ESPRIT, etc) and may be implemented withembodiments described herein instead of the Capon. Evaluation oflocations on the plots in FIGS. 2 and 3 that yield maximum powercorrelation values with the Capon search alone and without any initialestimates identifies the lines 200, 300, 302 under only some conditionsbut tends to fail or become unreliable in other applications in whichthe flowmeter 100 may be utilized. In some cases, a first pass of theCapon algorithm (see, FIG. 7) alone over a wide dynamic range (e.g., twoorders of magnitude) may produce multiple peaks obscuring a true peak.Further, low frequency noise and speed of sound velocities mayadditionally prevent distinguishing a relatively weaker vortical ridgehaving a peak that is not associated with the maximum power correlation.

Therefore, FIG. 4 illustrates sensing methods performed with theprocessing unit 108 for determining speed of sound and flow velocitythat improve ability to identify the lines 200, 300, 302. As with priorapproaches, signal entry step 400 involves receiving input data from thepressure sensors 101, 102, 103 so that initial processing step 402 canconvert, with the FFT logic 110, the data to frequency domain blocks ofdata and subsequently apply and thereafter invert a CSD function. Inboth velocity and speed of sound estimation steps 406, 407, calculationlogic 114 then probes matrices produced in the initial processing step402 using, for example, the Capon method to measure the powercorrelation of the signals at time shifts associated with an identifiedvelocity or speed of sound. In other words, the time shift as related toflow velocity refers to the fact that a particular power phenomenareceived at the third sensor 103 from the fluid 105 is received later intime at the second sensor 102 as the fluid 105 flows from the thirdsensor 103 to the second sensor 102. For some embodiments, the velocityestimation step 406 processes a first subset of vectors corresponding tothe velocity while a second subset of sensor data related to thespeed-of-sound is passed to the speed of sound estimation step 407.Inputs for a curve fit routine (see, FIG. 5) include power correlationvalues at one of multiple trial velocities or speeds of sound andseveral steps on either side utilizing data obtained from the sensors101, 102, 103. The velocity at which the curve fit routine returns amaximum curvature result corresponds to an estimate value of a speed ofsound in the fluid and/or a velocity of the flow.

For the velocity estimation step 406, multiplying results of the curvefit routine for each trial velocity by a respective directional qualitycompensation factor (Q_(trial)) calculated at each trial value helps toavoid misidentification of noise appearing symmetrically in bothpositive and negative directions instead of the vortical ridge since thevortical ridge extends in only one direction. Referring to FIG. 2,differences in power along the flow velocity line 200 with respect to apositive and directionally opposite dashed line 204 indicates that thepower along the flow velocity line 200 is not attributed to symmetricalnoise. As symmetry in power between positive and negative directionsdecreases, the compensation factor or an absolute value of the factorapproaches one, while the compensation factor goes to zero withincreasing symmetry. Therefore, the product of the directional qualitycompensation factor and the power curvature for each trial value adjuststhe results of the curve fit routine prior to determining the velocityestimate corresponding to this product. For some embodiments, a ratio ofthe difference and the sum of a first power correlation functionP(V_(trial)) for a given velocity trial and a second power correlationfunction P(−V_(trial)) for an opposite velocity negative of the givenvelocity trial defines the directional quality compensation factor asfollows:

$Q_{trial} = {\frac{{P\left( V_{trial} \right)} - {P\left( {- V_{trial}} \right)}}{{P\left( V_{trial} \right)} + {P\left( {- V_{trial}} \right)}}.}$

When a velocity quality metric such as the directional qualitycompensation factor falls below values of approximately 0.4 at thevelocity estimate, any flow velocity calculation results may lacksufficient confidence levels. The software in the processing unit 108may thus include a low-quality cutoff setting. With this cutoff, areported value of zero or error at the output 118 may occur if thevelocity quality metric is below a configured limit.

To ensure quality in the results for speed of sound, a speed of soundquality metric may yield a similar range of values as the directionalquality compensation factor. Values for speed of sound quality(Q_(curv)) approach one for high values of curvature when

${Q_{curv} = {\frac{C}{{abs}(C)} \times {\mathbb{e}}^{({{- 50}/{{abs}{(C)}}})}}},$where C is a coefficient in a least squares equation described furtherherein. The speed of sound quality metric includes an arbitrary value offifty which is near the lower limit for the “acceptable” range ofcurvature values. A low quality cutoff for reporting purposes may bearound 0.3 or about 0.25 at the speed of sound estimate.

Once the curvature in power as a function of each of the velocity trialsreveals the approximate location of a power peak that corresponds to theflow velocity estimate or speed of sound estimate, a conventional arrayprocessing algorithm may evaluate with the ridge identifier logic 116power correlation values associated with velocities or speeds around thevelocity estimate or the speed of sound estimate in refining velocityand speed of sound steps 408, 409. In some embodiments, the powercorrelation of the frequency to wave number domain is evaluated viaCapon routines at a finer resolution relative to increments betweentrial velocities and over a range of ±20% relative to the estimates. Ifa new-found power peak from this subsequent scan differs by more thanhalf the increment size, then the set of frequencies used is adjusted tocoincide with the new-found peak. The velocity having the highest powerresult is used as the center for the next pass of power correlations atan even finer resolution. For example, this refinement process mayrepeat three times, with the velocity increment size reduced by a factorof eight for each repetition. At the end of this refinement, velocityand speed of sound output steps 410, 411 select a final velocity andspeed of sound associated with maximums of the power correlation valuefrom a last scan. The output 118 then indicates the final velocityand/or speed of sound to the user or another device.

FIG. 5 shows a process of determining the velocity (which may be thevortical flow or the speed of sound) estimate utilized in the estimationsteps 406, 407. Multiple trial velocities (V_(trials)) selected in afirst pass step 500 identifies selected velocities at increments of, forexample, 5% through an entire range of possible velocities. Thefrequency range dynamically reflects changing velocity conditions. Foran array with sensor spacing Δx, the spatial aliasing frequency (spatialNyquist) for velocity is given by:

$f_{N} = {\frac{V}{\Delta\; x}.}$The frequency range set in a frequency selection step 502 corresponds toa fraction of this Nyquist frequency to avoid aliasing and common-modesignals. In some embodiments, these fractional amounts may identifyminimum and maximum frequencies for all power correlations associatedwith each respective V_(trial) picked for the vortical flow withf_(min)=0.3f_(N) and f_(max)=0.7f_(N)

The frequency range explored by the power correlations for the speed ofsound may also vary to adapt for each of the V_(trials). Range ofacoustic frequencies measured depends on the sensor array dimensions andthe speed of sound in the fluid as follows:

$\lambda_{\max} = {\left. {K \times L}\Rightarrow f_{\min} \right. = {\frac{SoS}{\lambda_{\max}} = {\left. {\frac{1}{K \times N}f_{N}}\rightarrow f_{\min} \right. = {{.083}\mspace{11mu} f_{N}}}}}$and${\lambda_{\min} = {\left. {2 \times \Delta\; x}\Rightarrow f_{\max} \right. = {\frac{SoS}{\lambda_{\min}} = {{.5}\mspace{11mu} f_{N}}}}},$where K is a factor such as 4 or 5 that determines the largestmeasurable wavelength, L is the aperture length between most upstreamand most downstream sensor (as shown, the third sensor 103 and the firstsensor 101) with N being the number of sensors (as shown, three), andthe factor two in the expression being a Nyquist based factor. Thefrequency selection step 502 thus may set appropriate limits in terms ofthe spatial Nyquist frequency for the sensor spacing and V_(trial) inspeed of sound determinations as with the flow velocity determinations.

Once the frequency range is set for a first V_(trial), initial powercorrelation step 504 measures the magnitude of the power correspondingto the first V_(trial) within the frequency limits. The initial powercorrelation step 504 involves sampling and summing spaced frequency binsbetween the f_(min) and f_(max) for the first V_(trial). Graphically,FIG. 2 shows a solid dotted line 206 representing the first V_(trial).Some of the dots may symbolize these bins along the slope for the firstV_(trial).

Next, additional power correlations step 506 samples and sums the sameset of frequency bins, in some embodiments, as utilized in the initialpower correlation step 504 at several (e.g., about 7 to 9) velocityincrements (V_(trial+n), V_(trial−n)) on either side of the firstV_(trial). This technique may be referred to as “dithering” or“jittering” the velocity of each V_(trial). For some embodiments,selection of the velocity increments equally spaces all velocityincrements from one another. The velocity increments may span a rangesuitable to detect sharp falloffs on either side of a peak, such as 90%to 110% of each V_(trial). Since this increment range is identified as apercentage of the V_(trial), the effects from power correlation ridgewidth differences at lower versus higher flow velocities tends to beequalized, yielding similar curvature values at all velocities. Withrespect to FIG. 2, maintaining the same frequencies and hence adjustingwave number produces slopes and frequency bins such as represented byfirst and second open dotted lines 208, 210 that may correspondrespectively to one of the V_(trial+n) and one of the V_(trial−n).

Curvature step 508 fits results from the initial power correlation step504 and the additional power correlations step 506 to a curve based onpower correlation values measured at the first V_(trial) and each of theV_(trial+n), V_(trial−n) associated with the first V_(trial). Inputsfrom all V_(trials) selected in the first pass step 500 thereby resultin generation of multiple independent curves at the curvature step 508with a corresponding curve for each V_(trial). For some embodiments, asecond-order least-squares curve fit routine, such asy=a+b*x+c*x ²,where y represents power inputs and x corresponds to velocity inputs,enables calculating curvature values, which correspond to respectiveones of the trial velocities. In some embodiments, each least squarescurve fit is calculated using “normalized” (x,y) coordinates instead ofwhat would be the “true” coordinates. Using the “true” coordinates mayyield curvature values that are higher at low velocities than at highvelocities. Referring back to FIG. 1, the calculation logic 114determines a negative of this “curvature” of power functions evaluatedaround each corresponding trial velocity by, for example, defining the“c” coefficient of the curve fit routine as a curvature value.

An end step 510 recognizes when all the multiple trial velocitiesidentified and selected in the first pass step 500 have beeninterrogated and hence all curves generated in the curvature step 508. Aridge peak velocity for speed of sound or vortical flow occurs close tothe largest negative curvature value which is associated with one of thetrial velocities. From the curves, estimation output step 512 thus picksone (or two, i.e., positive and negative, in the case of speed of sound)of the trial velocities with a max curvature or curvature value, as maybe identified by the negative of the “c” coefficient. As previouslydiscussed, the curvature values may be multiplied by the directionalquality compensation factor prior to picking the trial velocities with amaximum calculated value. Regardless, picked V_(trial(s)) establish thevelocity estimate or the speed of sound estimate.

For some embodiments, a prior final velocity from a previous measurementin time utilized for a current estimate enables truncation of themethods described herein once an initial measurement is taken asdiscussed heretofore. For example, the prior final velocity may providethe current estimate unless a quality metric returns below a threshold.In some embodiments, the prior final velocity may enable establishing arelatively narrower range of velocities scanned in the first pass step500 than searched in the previous measurement.

For visualization, the curve associated with the first V_(trial)represented by the solid dotted line 206 in FIG. 2 produces a relativelylow curvature compared to that of the flow velocity line 200 as there isno significant pattern among differences in power between any of thedotted lines 206, 208, 210. The directional quality metric furtherreduces any curvature present as calculated for the first V_(trial)represented by the solid dotted line 206 due to substantial symmetricalnoise also present oppositely in the positive direction. Even whenconventional power correlation scanning alone may fail to identify oraccurately identify velocities, max curvature based analysis of trialvelocities enables a less obscured and more accurate determination ofvortical velocity or speed of sound.

FIG. 6 shows an estimation curve 600 that plots products obtained bymultiplying a respective directional quality compensation factor times amax value of power curvature for each velocity step evaluated accordingto the process depicted in FIG. 5 versus velocity. A low value productpoint 604 lacks one or both of unidirectionality or a high curvaturevalue similar to the first V_(trial) represented by the solid dottedline 206 in FIG. 2. By contrast, a high value product point 602 derivesfrom a high curvature value, similar to one taken at flow velocity line200, which is not negated by the directional quality compensation factorbeing indicative of high symmetry.

FIG. 7 illustrates a plot of power versus velocity showing a total scancurve 700 which is obtained by searching converted data from pressuresensors and a targeted scan curve 702 of the converted data around avelocity estimate picked by results plotted in FIG. 6 to identify afinal velocity 704 at a peak centered at 103 feet per second. The totalscan curve 700 includes multiple peaks with the peak of the finalvelocity not being a maximum peak thereby obscuring results taken fromthe total scan curve 700 alone. However, identification of the peak ofthe final velocity appears clear and distinct in FIG. 6 even thoughFIGS. 6 and 7 are plots taken based on the same fluid flow.

For reference, the estimation curve 600 in FIG. 6 corresponds to dataevaluated in the calculation logic 114 in FIG. 1 and utilized in thevelocity estimation step 406 in FIG. 4 as derived from the process inFIG. 5. The estimation curve 600 replaces the total scan curve 700 thatis not required and is only shown for illustration purposes. Further,the targeted scan curve 702 represents data used in the refiningvelocity step 408 in FIG. 4 and examined in the ridge identifier logic116 in FIG. 1.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A sensing system for measuring a flow velocity of a fluid in aconduit, comprising: an array of at least two pressure sensors spacedalong the conduit to output signals indicative of dynamic pressurevariations of the fluid; and a signal processor configured to processthe signals by probing trial velocities, wherein the probing includescalculating curvatures associated with power correlations of the signalsdetermined around the trial velocities and selecting one of the trialvelocities corresponding to a maximum curvature value from thecurvatures as a measurement for the flow velocity.
 2. The sensing systemof claim 1, wherein the probing further comprises for each trial valueselecting minimum and maximum frequencies that remain constant for allof the power correlations used to derive one of the curvatures.
 3. Thesensing system of claim 1, wherein the probing further comprisescalculating the curvatures by fitting data from the power correlationsto a least squares polynomial.
 4. The sensing system of claim 3, whereinthe polynomial is defined as y=a+b*x+c*x², wherein “y” represents apower input, “x” corresponds to a velocity input, and “a,” “b,” and “c”are coefficients, with the maximum curvature value being a negative ofthe “c” coefficient that is greatest.
 5. The sensing system of claim 1,wherein the signal processor further applies a respective directionalquality metric to adjust curvature values from each of the curvaturesbased on symmetry of power in negative and positive directions prior toselecting one of the trial velocities corresponding to the maximumcurvature value.
 6. The sensing system of claim 1, wherein the signalprocessor is configured to further process the signals by scanning finalvelocity power correlations within a percentage of the measurement forthe flow velocity and at a finer resolution than the probing such that aridge identified in the scanning corresponds to a final value of theflow velocity.
 7. The sensing system of claim 1, wherein the signalprocessor is further configured to probe trial speeds of sound bycalculating curvatures associated with power correlations of the signalsdetermined around the trial speeds of sound and selecting a positive anda negative one of the trial speeds of sound corresponding to arespective maximum curvature value from the curvatures associated with apositive direction and from the curvatures associated with a negativedirection.
 8. A sensing system for measuring a parameter of a fluid in aconduit, comprising: an array of at least two pressure sensors spacedalong the conduit to output signals indicative of dynamic pressurevariations of the fluid; and a signal processor configured to processthe signals by probing trial velocities, wherein the probing includescalculating curvatures associated with power correlations of the signalsdetermined around the trial velocities and selecting one of the trialvelocities corresponding to a maximum curvature value from thecurvatures as a measurement for the parameter.
 9. The sensing system ofclaim 8, further comprising an output to communicate the measurement forthe parameter.
 10. The sensing system of claim 9, wherein the signalprocessor further applies a quality metric to the maximum curvaturevalue and only provides the measurement for the parameter to the outputif a result from the quality metric is above a threshold value.
 11. Thesensing system of claim 8, further comprising a display configured toshow at least one of a speed of sound in the fluid and a flow velocityof the fluid as determined based on the measurement for the parameter.12. A sensing system for measuring a parameter of a fluid in a conduit,comprising: an array of at least two pressure sensors spaced along theconduit for outputting signals indicative of dynamic pressure variationsof the fluid; and a signal processor configured to process the signalsby performing a method of probing the signals at first and second trialvelocities that includes: measuring first power correlationscorresponding to the first trial velocity and first incrementalvelocities on either side of the first trial velocity to produce firstdata of the first power correlations; measuring second powercorrelations corresponding to the second trial velocity and secondincremental velocities on either side of the second trial velocity toproduce second data of the second power correlations; calculating firstand second curvatures for the first and second data, respectively;selecting which one of the first and second trial velocities correspondsto a greater of the first and second curvatures and represents anestimate value for the parameter; and outputting the estimate value. 13.The sensing system of claim 12, wherein the parameter is at least one ofa flow velocity of the fluid and a speed of sound in the fluid.
 14. Thesensing system of claim 12, wherein calculating each of the curvaturesoccurs by fitting a respective one of the data to a least squarespolynomial.
 15. A method of measuring a parameter of a fluid in aconduit, comprising: sensing, with an array of at least two pressuresensors, at spaced locations along the conduit dynamic pressurevariations of the fluid to provide signals indicative of the pressurevariations; and processing the signals by probing trial velocities,wherein the probing includes calculating power curvatures associatedwith power correlations of the signals determined around the trialvelocities and selecting one of the trial velocities corresponding to amaximum curvature value from the power curvatures as a measurement forthe parameter.
 16. The method of claim 15, further comprising at leastone of outputting the measurement for the parameter, generating a signalbased on the measurement for the parameter, or controlling an apparatusbased on the measurement for the parameter.
 17. The method of claim 15,further comprising outputting the measurement for the parameter toprovide an estimate for further processing of the signals.
 18. Themethod of claim 15, further comprising outputting a flow velocity of thefluid based on the measurement for the parameter.
 19. The method ofclaim 15, further comprising outputting a speed of sound in the fluidbased on the measurement for the parameter.
 20. The method of claim 15,further comprising selecting for each trial value minimum and maximumfrequencies that remain constant for all of the power correlations usedto derive one of the curvatures.